The conversation around machine learning infrastructure defaults immediately to GPUs. NVIDIA A100s, H100s, cloud GPU instances. That framing is accurate for a specific category of work: training large neural networks, particularly transformers and large vision models, where matrix multiplication throughput determines job completion time.
It is not accurate for a substantial portion of real-world ML work. Feature engineering, classical ML, hyperparameter tuning, inference serving, and gradient boosting do not need GPU acceleration. They need fast CPUs, large RAM, and storage I/O that does not throttle mid-job. That is exactly the profile of a dedicated bare metal server.
The GPU vs. CPU Decision for ML Workloads
When GPU Is the Right Choice
Neural network training with large parameter counts benefits from GPU parallelism because modern deep learning frameworks decompose training into matrix operations that GPUs execute thousands of times faster than CPUs. A convolutional neural network training on ImageNet, a BERT fine-tune on a large corpus, a diffusion model for image generation: these are GPU workloads. Running them on CPU is technically possible and practically unusable for anything beyond toy dataset sizes.
Cloud GPU instances make sense when GPU jobs are infrequent, and you cannot justify owning dedicated GPU hardware. A100 instances at $3-10 per hour work fine for occasional training runs.
When CPU Dedicated Servers Win
Several ML workload categories run faster on high-core-count CPUs than on cloud GPU instances, and much more cheaply:
Gradient boosting (XGBoost, LightGBM, CatBoost): These frameworks are heavily optimized for CPU parallelism. XGBoost training on 16 cores with OpenMP is genuinely fast. GPU acceleration for gradient boosting is beneficial for very large datasets, but it adds complexity and cost that rarely pays off for datasets under 10GB.
scikit-learn at scale: Random forests, SVMs, and ensemble methods parallelize across CPU cores. A 16-core EPYC handles cross-validated grid search across hundreds of parameter combinations in the time a single-core cloud instance would take to complete a fraction of them.
Feature engineering pipelines: Pandas, Polars, and Spark-based feature computation is memory-intensive and I/O-bound. 192GB of RAM means your entire feature matrix stays in memory. NVMe storage means data loading from Parquet files does not become the bottleneck.
Hyperparameter tuning: Running 16 parallel Optuna trials, each as a single-threaded training job, uses all 16 cores simultaneously. This beats paying for a GPU instance that sits mostly idle during the evaluation phase of each trial.
ML inference serving: Most production inference endpoints for classical ML, tabular models, and small neural networks run on CPU. Latency requirements are typically in the tens of milliseconds, which CPU inference handles comfortably.
AMD EPYC 4545P for Machine Learning
Architecture Advantages
The AMD EPYC 4545P is a 16-core, 32-thread processor based on AMD’s Zen 4 architecture. For ML workloads, three architectural characteristics matter:
AVX-512 support: Enables vectorized floating-point operations for numerical libraries like NumPy and Intel MKL. ML frameworks compiled with AVX-512 support run 20-40% faster on compatible CPUs vs. systems limited to AVX-256.
Large L3 cache: Reduces memory access latency for frequently accessed model parameters and feature data during training loops. XGBoost training in particular benefits from cache-resident data structures.
DDR5 memory bandwidth: DDR5 provides roughly 1.5x the theoretical bandwidth of DDR4. For memory-bandwidth-bound operations like large matrix multiplies on a CPU, that bandwidth directly translates to faster computation.
These are not marginal differences. On XGBoost training with a 5GB dataset, the combination of 16 cores, AVX-512, and DDR5 bandwidth on an EPYC system produces results competitive with GPU-accelerated training for this specific workload class.
Memory Capacity for Dataset Loading
The most common practical bottleneck in CPU-based ML is loading data. A feature matrix with 50 million rows and 200 features in float32 format occupies roughly 40GB of RAM. On a system with 32GB, that dataset requires chunked loading, which adds complexity to every training pipeline step.
On a 192GB system, that 40GB feature matrix loads once and stays in memory across cross-validation folds, multiple algorithm comparisons, and feature importance analysis. No chunking. No reloading between experiments. The experiment iteration speed improvement is substantial, particularly during exploratory phases.
TensorFlow and PyTorch on CPU
CPU Training for Smaller Neural Networks
Not all neural network training requires a GPU. Shallow networks, tabular neural networks (TabNet, SAINT), and small sequence models trained on moderate-sized datasets run acceptably on CPU when the model parameter count stays below roughly 10-50 million parameters.
PyTorch uses OpenMP for CPU parallelism. Setting OMP_NUM_THREADS=16 and torch.set_num_threads(16) on a 16-core system ensures PyTorch training uses all available cores. For batch sizes that fit in CPU cache (small models, dense layers), training throughput is higher than commonly assumed.
TensorFlow’s CPU optimization uses Intel MKL-DNN (oneDNN), which benefits from AVX-512 on compatible hardware. TensorFlow builds with AVX-512 run measurably faster on EPYC than on older Xeon systems that pre-date AVX-512 support.
Data Loading as the Real Bottleneck
For CPU training loops, the data loader is frequently slower than the model forward pass. PyTorch DataLoaders with num_workers=8 or higher on a 16-core system can pre-fetch batches fast enough to keep the training loop from starving.
NVMe storage changes this calculation further. Reading Parquet files for batched training on a SATA SSD system stalls frequently. On NVMe at 5GB/s sequential read, data loader threads keep ahead of the training loop even at high batch sizes. This matters for models trained on image data, text tokenized from disk, or tabular data from large Parquet partitions.
Hyperparameter Tuning Parallelism
Hyperparameter search is embarrassingly parallel. Each trial is independent. On a 16-core system, you can run 16 trials simultaneously without any inter-process communication overhead.
Optuna, Ray Tune, and scikit-learn’s GridSearchCV all support parallel trial execution through Python’s multiprocessing. A grid search across 256 parameter combinations that takes 8 hours single-threaded completes in roughly 30 minutes on 16 cores. That compression changes how ML teams iterate.
Cloud alternatives for this use case either charge per-core pricing that adds up quickly for long searches or require spinning up distributed Ray clusters with the associated management overhead. A dedicated server running all 16 cores for a 30-minute search has no cluster coordination cost.
Inference Serving Infrastructure
Latency and Throughput for Production Endpoints
Most production ML inference endpoints do not need a GPU. Tabular models, recommendation engines, fraud detection classifiers, and NLP models with sub-100M parameters serve requests in 1-20ms on modern CPUs. GPU inference adds infrastructure cost and complexity that is not justified unless you are serving large language models or image generation.
A dedicated server running FastAPI or TorchServe handles several hundred to a few thousand inference requests per second for typical tabular ML models, depending on model complexity and feature computation overhead. For teams currently paying for GPU inference instances serving relatively small models, migrating to CPU inference on dedicated hardware typically cuts inference infrastructure costs by 60-80%.
Model Caching and Memory
Keeping multiple model versions loaded in memory simultaneously requires RAM. An ML platform serving 10 different model versions, each 2-4GB in size, needs 20-40GB of memory just for model caching before counting request handling overhead. At 192GB, you have room for a full model registry in memory, plus the application layer and operating system, with no contention.
Cost Comparison: ML Infrastructure Options
What Stays on Cloud GPU
Dedicated CPU servers do not replace cloud GPUs for every workload. Large language model fine-tuning, training vision transformers from scratch, and stable diffusion training remain GPU-bound. The practical approach for most ML teams:
Run feature engineering, EDA, gradient boosting, and inference on dedicated CPU infrastructure
Use cloud GPU spot instances for periodic deep learning training jobs
Use dedicated CPU inference for all models except very large neural networks
Stage data preprocessing pipelines on NVMe-backed dedicated storage rather than cloud object storage
This hybrid approach captures the cost and performance advantages of both options without forcing a single infrastructure choice onto workloads with different requirements.
Getting Set Up
InMotion Hosting’s Extreme Dedicated Server ships with the AMD EPYC 4545P, 192GB DDR5 ECC RAM, and dual 3.84TB NVMe SSDs managed by InMotion Hosting’s APS team. Python environment setup, CUDA (if you later add an external GPU), and ML framework installation fall into the standard server setup that InMotion Solutions can assist with under Premier Care.
Explore dedicated servers: inmotionhosting.com/dedicated-servers
Compare pricing tiers: inmotionhosting.com/dedicated-servers/dedicated-server-price
Premier Care details: inmotionhosting.com/blog/inmotion-premier-care/
For teams spending over $300 per month on cloud CPU instances for ML workloads, the transition to dedicated hardware pays for itself in the first billing cycle.